




'''simple docstring'''







import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowerCAmelCase_     						=				'''sshleifer/mar_enro_6_3_student'''

class 	_snake_case(			UpperCAmelCase      ):






 def 						_UpperCamelCase (self       :  Dict    )       ->   Union[str, Any]:






    """simple docstring"""


    super().setUp()

    A__       =     cached_path(
        'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'    ,		extract_compressed_file=a    ,		)
    A__       =     f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






 @slow
 @require_torch_gpu
 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[Any]:






    """simple docstring"""


    MarianMTModel.from_pretrained(a    )






 @slow
 @require_torch_gpu
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   List[str]:






    """simple docstring"""


    A__       =     {
        '$MAX_LEN': 64,
        '$BS': 64,
        '$GAS': 1,
        '$ENRO_DIR': self.data_dir,
        'facebook/mbart-large-cc25': MARIAN_MODEL,
        # "val_check_interval=0.25": "val_check_interval=1.0",
        '--learning_rate=3e-5': '--learning_rate 3e-4',
        '--num_train_epochs 6': '--num_train_epochs 1',
    }

    # Clean up bash script
    A__       =     (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'    )[1].strip()
    A__       =     bash_script.replace('\\\n'    ,		''    ).strip().replace('"$@"'    ,		''    )
    for k, v in env_vars_to_replace.items():
       A__       =     bash_script.replace(a    ,		str(a    )    )
    A__       =     self.get_auto_remove_tmp_dir()

    # bash_script = bash_script.replace("--fp16 ", "")
    A__       =     f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
    # XXX: args.gpus > 1 : handle multi_gpu in the future

    A__       =     ['finetune.py'] + bash_script.split() + args
    with patch.object(a    ,		'argv'    ,		a    ):
       A__       =     argparse.ArgumentParser()
       A__       =     pl.Trainer.add_argparse_args(a    )
       A__       =     SummarizationModule.add_model_specific_args(a    ,		os.getcwd()    )
       A__       =     parser.parse_args()
       A__       =     main(a    )

    # Check metrics
    A__       =     load_json(model.metrics_save_path    )
    A__       =     metrics['val'][0]
    A__       =     metrics['val'][-1]
    self.assertEqual(len(metrics['val']    )    ,		(args.max_epochs / args.val_check_interval)    )
    assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]    ,		a    )

    self.assertGreater(last_step_stats['val_avg_gen_time']    ,		0.01    )
    # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
    self.assertLessEqual(last_step_stats['val_avg_gen_time']    ,		1.0    )

    # test learning requirements:

    # 1. BLEU improves over the course of training by more than 2 pts
    self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']    ,		2    )

    # 2. BLEU finishes above 17
    self.assertGreater(last_step_stats['val_avg_bleu']    ,		17    )

    # 3. test BLEU and val BLEU within ~1.1 pt.
    self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']    )    ,		1.1    )

    # check lightning ckpt can be loaded and has a reasonable statedict
    A__       =     os.listdir(a    )
    A__       =     [x for x in contents if x.endswith('.ckpt'    )][0]
    A__       =     os.path.join(args.output_dir    ,		a    )
    A__       =     torch.load(a    ,		map_location='cpu'    )
    A__       =     'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
       A__       =     {os.path.basename(a    ) for p in contents}
       assert "test_generations.txt" in contents
       assert "test_results.txt" in contents
       # assert len(metrics["val"]) ==  desired_n_evals
       assert len(metrics['test']    ) == 1




class 	_snake_case(			UpperCAmelCase      ):






 @timeout_decorator.timeout(6_00    )
 @slow
 @require_torch_gpu
 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
    A__       =     {
        '--fp16_opt_level=O1': '',
        '$MAX_LEN': 1_28,
        '$BS': 16,
        '$GAS': 1,
        '$ENRO_DIR': data_dir,
        '$m': 'sshleifer/student_marian_en_ro_6_1',
        'val_check_interval=0.25': 'val_check_interval=1.0',
    }

    # Clean up bash script
    A__       =     (
        (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'    )[1].strip()
    )
    A__       =     bash_script.replace('\\\n'    ,		''    ).strip().replace('"$@"'    ,		''    )
    A__       =     bash_script.replace('--fp16 '    ,		' '    )

    for k, v in env_vars_to_replace.items():
       A__       =     bash_script.replace(a    ,		str(a    )    )
    A__       =     self.get_auto_remove_tmp_dir()
    A__       =     bash_script.replace('--fp16'    ,		''    )
    A__       =     6
    A__       =     (
        ['distillation.py']
        + bash_script.split()
        + [
            f"""--output_dir={output_dir}""",
            '--gpus=1',
            '--learning_rate=1e-3',
            f"""--num_train_epochs={epochs}""",
            '--warmup_steps=10',
            '--val_check_interval=1.0',
            '--do_predict',
        ]
    )
    with patch.object(a    ,		'argv'    ,		a    ):
       A__       =     argparse.ArgumentParser()
       A__       =     pl.Trainer.add_argparse_args(a    )
       A__       =     SummarizationDistiller.add_model_specific_args(a    ,		os.getcwd()    )
       A__       =     parser.parse_args()
       # assert args.gpus == gpus THIS BREAKS for multi_gpu

       A__       =     distill_main(a    )

    # Check metrics
    A__       =     load_json(model.metrics_save_path    )
    A__       =     metrics['val'][0]
    A__       =     metrics['val'][-1]
    assert len(metrics['val']    ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

    assert last_step_stats["val_avg_gen_time"] >= 0.01

    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
    assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]    ,		a    )

    # check lightning ckpt can be loaded and has a reasonable statedict
    A__       =     os.listdir(a    )
    A__       =     [x for x in contents if x.endswith('.ckpt'    )][0]
    A__       =     os.path.join(args.output_dir    ,		a    )
    A__       =     torch.load(a    ,		map_location='cpu'    )
    A__       =     'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
    assert expected_key in ckpt["state_dict"]
    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

    # TODO: turn on args.do_predict when PL bug fixed.
    if args.do_predict:
       A__       =     {os.path.basename(a    ) for p in contents}
       assert "test_generations.txt" in contents
       assert "test_results.txt" in contents
       # assert len(metrics["val"]) ==  desired_n_evals
       assert len(metrics['test']    ) == 1








'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


